package com.example.mp4togift.has;

/**
 * @author 林丹荣
 * 创建日期：2020/10/20
 * desc：
 */
public class NeuQuant {
    private static final int netsize = 256; /* number of colours used */



    /* four primes near 500 - assume no image has a length so large */

    /* that it is divisible by all four primes */

    private static final int prime1 = 499;

    private static final int prime2 = 491;

    private static final int prime3 = 487;

    private static final int prime4 = 503;



    private static final int minpicturebytes = (3 * prime4);

    /* minimum size for input image */



    private static final int maxnetpos = (netsize - 1);

    private static final int netbiasshift = 4; /* bias for colour values */

    private static final int ncycles = 100; /* no. of learning cycles */



    /* defs for freq and bias */

    private static final int intbiasshift = 16; /* bias for fractions */

    private static final int intbias = (1 << intbiasshift);

    private static final int gammashift = 10; /* gamma = 1024 */

    private static final int gamma = (1 << gammashift);

    private static final int betashift = 10;

    private static final int beta = (intbias >> betashift); /* beta = 1/1024 */

    private static final int betagamma = (intbias << (gammashift - betashift));



    /* defs for decreasing radius factor */

    private static final int initrad = (netsize >> 3); /* for 256 cols, radius starts */

    private static final int radiusbiasshift = 6; /* at 32.0 biased by 6 bits */

    private static final int radiusbias = (1 << radiusbiasshift);

    private static final int initradius = (initrad * radiusbias); /* and decreases by a */

    private static final int radiusdec = 30; /* factor of 1/30 each cycle */



    /* defs for decreasing alpha factor */

    private static final int alphabiasshift = 10; /* alpha starts at 1.0 */

    private static final int initalpha = (1 << alphabiasshift);



    private int alphadec; /* biased by 10 bits */



    /* radbias and alpharadbias used for radpower calculation */

    private static final int radbiasshift = 8;

    private static final int radbias = (1 << radbiasshift);

    private static final int alpharadbshift = (alphabiasshift + radbiasshift);

    private static final int alpharadbias = (1 << alpharadbshift);



    private byte[] thepicture; /* the input image itself */

    private int lengthcount; /* lengthcount = H*W*3 */



    private int samplefac; /* sampling factor 1..30 */



    //   typedef int pixel[4];                /* BGRc */

    private int[][] network; /* the network itself - [netsize][4] */



    private int[] netindex = new int[256];

    /* for network lookup - really 256 */



    private int[] bias = new int[netsize];

    /* bias and freq arrays for learning */

    private int[] freq = new int[netsize];

    private int[] radpower = new int[initrad];

    /* radpower for precomputation */



  /* Initialise network in range (0,0,0) to (255,255,255) and set parameters
     ----------------------------------------------------------------------- */

    public NeuQuant(byte[] thepic, int len, int sample) {



        int i;

        int[] p;



        thepicture = thepic;

        lengthcount = len;

        samplefac = sample;



        network = new int[netsize][];

        for (i = 0; i < netsize; i++) {

            network[i] = new int[4];

            p = network[i];

            p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;

            freq[i] = intbias / netsize; /* 1/netsize */

            bias[i] = 0;

        }

    }



    public byte[] colorMap() {

        byte[] map = new byte[3 * netsize];

        int[] index = new int[netsize];

        for (int i = 0; i < netsize; i++)

            index[network[i][3]] = i;

        int k = 0;

        for (int i = 0; i < netsize; i++) {

            int j = index[i];

            map[k++] = (byte) (network[j][0]);

            map[k++] = (byte) (network[j][1]);

            map[k++] = (byte) (network[j][2]);

        }

        return map;

    }



  /* Insertion sort of network and building of netindex[0..255] (to do after unbias)
     ------------------------------------------------------------------------------- */

    public void inxbuild() {



        int i, j, smallpos, smallval;

        int[] p;

        int[] q;

        int previouscol, startpos;



        previouscol = 0;

        startpos = 0;

        for (i = 0; i < netsize; i++) {

            p = network[i];

            smallpos = i;

            smallval = p[1]; /* index on g */

            /* find smallest in i..netsize-1 */

            for (j = i + 1; j < netsize; j++) {

                q = network[j];

                if (q[1] < smallval) { /* index on g */

                    smallpos = j;

                    smallval = q[1]; /* index on g */

                }

            }

            q = network[smallpos];

            /* swap p (i) and q (smallpos) entries */

            if (i != smallpos) {

                j = q[0];

                q[0] = p[0];

                p[0] = j;

                j = q[1];

                q[1] = p[1];

                p[1] = j;

                j = q[2];

                q[2] = p[2];

                p[2] = j;

                j = q[3];

                q[3] = p[3];

                p[3] = j;

            }

            /* smallval entry is now in position i */

            if (smallval != previouscol) {

                netindex[previouscol] = (startpos + i) >> 1;

                for (j = previouscol + 1; j < smallval; j++)

                    netindex[j] = i;

                previouscol = smallval;

                startpos = i;

            }

        }

        netindex[previouscol] = (startpos + maxnetpos) >> 1;

        for (j = previouscol + 1; j < 256; j++)

            netindex[j] = maxnetpos; /* really 256 */

    }



  /* Main Learning Loop
     ------------------ */

    public void learn() {



        int i, j, b, g, r;

        int radius, rad, alpha, step, delta, samplepixels;

        byte[] p;

        int pix, lim;



        if (lengthcount < minpicturebytes)

            samplefac = 1;

        alphadec = 30 + ((samplefac - 1) / 3);

        p = thepicture;

        pix = 0;

        lim = lengthcount;

        samplepixels = lengthcount / (3 * samplefac);

        delta = samplepixels / ncycles;

        alpha = initalpha;

        radius = initradius;



        rad = radius >> radiusbiasshift;

        if (rad <= 1)

            rad = 0;

        for (i = 0; i < rad; i++)

            radpower[i] =

                    alpha * (((rad * rad - i * i) * radbias) / (rad * rad));



        //fprintf(stderr,"beginning 1D learning: initial radius=%d\n", rad);



        if (lengthcount < minpicturebytes)

            step = 3;

        else if ((lengthcount % prime1) != 0)

            step = 3 * prime1;

        else {

            if ((lengthcount % prime2) != 0)

                step = 3 * prime2;

            else {

                if ((lengthcount % prime3) != 0)

                    step = 3 * prime3;

                else

                    step = 3 * prime4;

            }

        }



        i = 0;

        while (i < samplepixels) {

            b = (p[pix + 0] & 0xff) << netbiasshift;

            g = (p[pix + 1] & 0xff) << netbiasshift;

            r = (p[pix + 2] & 0xff) << netbiasshift;

            j = contest(b, g, r);



            altersingle(alpha, j, b, g, r);

            if (rad != 0)

                alterneigh(rad, j, b, g, r); /* alter neighbours */



            pix += step;

            if (pix >= lim)

                pix -= lengthcount;



            i++;

            if (delta == 0)

                delta = 1;

            if (i % delta == 0) {

                alpha -= alpha / alphadec;

                radius -= radius / radiusdec;

                rad = radius >> radiusbiasshift;

                if (rad <= 1)

                    rad = 0;

                for (j = 0; j < rad; j++)

                    radpower[j] =

                            alpha * (((rad * rad - j * j) * radbias) / (rad * rad));

            }

        }

        //fprintf(stderr,"finished 1D learning: final alpha=%f !\n",((float)alpha)/initalpha);

    }



  /* Search for BGR values 0..255 (after net is unbiased) and return colour index
     ---------------------------------------------------------------------------- */

    public int map(int b, int g, int r) {



        int i, j, dist, a, bestd;

        int[] p;

        int best;



        bestd = 1000; /* biggest possible dist is 256*3 */

        best = -1;

        i = netindex[g]; /* index on g */

        j = i - 1; /* start at netindex[g] and work outwards */



        while ((i < netsize) || (j >= 0)) {

            if (i < netsize) {

                p = network[i];

                dist = p[1] - g; /* inx key */

                if (dist >= bestd)

                    i = netsize; /* stop iter */

                else {

                    i++;

                    if (dist < 0)

                        dist = -dist;

                    a = p[0] - b;

                    if (a < 0)

                        a = -a;

                    dist += a;

                    if (dist < bestd) {

                        a = p[2] - r;

                        if (a < 0)

                            a = -a;

                        dist += a;

                        if (dist < bestd) {

                            bestd = dist;

                            best = p[3];

                        }

                    }

                }

            }

            if (j >= 0) {

                p = network[j];

                dist = g - p[1]; /* inx key - reverse dif */

                if (dist >= bestd)

                    j = -1; /* stop iter */

                else {

                    j--;

                    if (dist < 0)

                        dist = -dist;

                    a = p[0] - b;

                    if (a < 0)

                        a = -a;

                    dist += a;

                    if (dist < bestd) {

                        a = p[2] - r;

                        if (a < 0)

                            a = -a;

                        dist += a;

                        if (dist < bestd) {

                            bestd = dist;

                            best = p[3];

                        }

                    }

                }

            }

        }

        return (best);

    }



    public byte[] process() {

        learn();

        unbiasnet();

        inxbuild();

        return colorMap();

    }



  /* Unbias network to give byte values 0..255 and record position i to prepare for sort
     ----------------------------------------------------------------------------------- */

    public void unbiasnet() {



        @SuppressWarnings("unused")

        int i, j;



        for (i = 0; i < netsize; i++) {

            network[i][0] >>= netbiasshift;

            network[i][1] >>= netbiasshift;

            network[i][2] >>= netbiasshift;

            network[i][3] = i; /* record colour no */

        }

    }



  /* Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
     --------------------------------------------------------------------------------- */

    private void alterneigh(int rad, int i, int b, int g, int r) {



        int j, k, lo, hi, a, m;

        int[] p;



        lo = i - rad;

        if (lo < -1)

            lo = -1;

        hi = i + rad;

        if (hi > netsize)

            hi = netsize;



        j = i + 1;

        k = i - 1;

        m = 1;

        while ((j < hi) || (k > lo)) {

            a = radpower[m++];

            if (j < hi) {

                p = network[j++];

                try {

                    p[0] -= (a * (p[0] - b)) / alpharadbias;

                    p[1] -= (a * (p[1] - g)) / alpharadbias;

                    p[2] -= (a * (p[2] - r)) / alpharadbias;

                } catch (Exception e) {

                } // prevents 1.3 miscompilation

            }

            if (k > lo) {

                p = network[k--];

                try {

                    p[0] -= (a * (p[0] - b)) / alpharadbias;

                    p[1] -= (a * (p[1] - g)) / alpharadbias;

                    p[2] -= (a * (p[2] - r)) / alpharadbias;

                } catch (Exception e) {

                }

            }

        }

    }



  /* Move neuron i towards biased (b,g,r) by factor alpha
     ---------------------------------------------------- */

    private void altersingle(int alpha, int i, int b, int g, int r) {



        /* alter hit neuron */

        int[] n = network[i];

        n[0] -= (alpha * (n[0] - b)) / initalpha;

        n[1] -= (alpha * (n[1] - g)) / initalpha;

        n[2] -= (alpha * (n[2] - r)) / initalpha;

    }



  /* Search for biased BGR values
     ---------------------------- */

    private int contest(int b, int g, int r) {



        /* finds closest neuron (min dist) and updates freq */

        /* finds best neuron (min dist-bias) and returns position */

        /* for frequently chosen neurons, freq[i] is high and bias[i] is negative */

        /* bias[i] = gamma*((1/netsize)-freq[i]) */



        int i, dist, a, biasdist, betafreq;

        int bestpos, bestbiaspos, bestd, bestbiasd;

        int[] n;



        bestd = ~(1 << 31);

        bestbiasd = bestd;

        bestpos = -1;

        bestbiaspos = bestpos;



        for (i = 0; i < netsize; i++) {

            n = network[i];

            dist = n[0] - b;

            if (dist < 0)

                dist = -dist;

            a = n[1] - g;

            if (a < 0)

                a = -a;

            dist += a;

            a = n[2] - r;

            if (a < 0)

                a = -a;

            dist += a;

            if (dist < bestd) {

                bestd = dist;

                bestpos = i;

            }

            biasdist = dist - ((bias[i]) >> (intbiasshift - netbiasshift));

            if (biasdist < bestbiasd) {

                bestbiasd = biasdist;

                bestbiaspos = i;

            }

            betafreq = (freq[i] >> betashift);

            freq[i] -= betafreq;

            bias[i] += (betafreq << gammashift);

        }

        freq[bestpos] += beta;

        bias[bestpos] -= betagamma;

        return (bestbiaspos);

    }
}
